Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …

This paper investigates how to use prediction strategies to improve the performance of
multiobjective evolutionary optimization algorithms in dealing with dynamic environments …

Abstract Dynamic Multi-objective Optimization Problem (DMOP) is emerging in recent years
as a major real-world optimization problem receiving considerable attention. Tracking the …

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-
dependent objective functions, while the ones with a changing number of objectives have …

Distributed database system (DDBS) technology has shown its advantages with respect to
query processing efficiency, scalability, and reliability. Moreover, by partitioning attributes of …

Dynamic multi-objective optimization problems (DMOPs), in which the environments change
over time, have attracted many researchers' attention in recent years. Since the Pareto set …

Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be
tested on benchmark functions to determine whether the algorithm can overcome specific …

Abstract Dynamic Multi-objective Optimization is a challenging research topic since the
objective functions, constraints, and problem parameters may change over time. Although …

In this paper, a novel population robustness-based switching response framework (PR-SRF)
is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA) …

A key issue in evolutionary algorithms for dynamic multi-objective optimization problems
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …